Earticle

초록

영어

To considered the problem of discovering of interesting association rule among item sets in data base. Algorithms for mining association rule are practical methods to find interesting rules implied in large database. Proposed an innovative approach, beyond minimum support and minimum confidence framework, some extra measures consider for rule improvement is user interestingness constraint. It use three user defined constraint minimum support, minimum confidence, and interesting item and in addition makes use of k- nonempty subset generation methodology of the item which are user interest. Proposed algorithm fundamentally different from the identified algorithms, a number of algorithm is developed for association rule mining, the identified algorithms go through as of number of scanning of data base, and generate the candidate item set , unnecessary or uninteresting rule . The current method applies the user interesting constraint to generate only interesting association rule in data base. Proposed approach not just reduces the number of scanning of data base but also generated frequent itemset, and mine interesting association rule. Experimental result shows that the number of uninteresting rules can be reduced successfully and the validity of rules which mined are better.

목차

Abstract
 1. Introduction
 2. Problem Statements
 3. Interestingness Constraints
 4. Algorithm with Interestingness Constraint
 5. Illustration
 6. Performance Evaluations
 7. Conclusion
 Acknowledgements
 References

키워드

저자

• Sangita Kalmodia [ Computer Science and Engineering, CMRIT/ VTU-Belgum, India ]
• Jitendranath Mungara [ Computer Science and Engineering, CMRIT/ VTU-Belgum, India ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605